The COVID-19 Pandemic and Its Impact on Patient Safety Incidents at a University Hospital: A Retrospective Study
Bibliographic record
Abstract
Background: The main reason for submitting safety incident reports at medical institutions is to prevent serious medical accidents. Even during novel coronavirus disease 2019 (COVID-19) pandemic, it is necessary to prevent serious medical accidents, so it is important to submit incident reports, analyze contributing factors, and work to prevent recurrence. Methods: We conducted a retrospective study of patient safety incidents reported by the Fukuoka University Hospital in Fukuoka City, Japan, and examined the changes in safety incident reports during the COVID-19 pandemic. Results: The main findings were as follows. First, the number of patient safety incidents reported per 10,000 patients during the pandemic tended to be higher than that of pre-pandemic period, and this trend was considered to be desirable. Second, during the peak of COVID-19 waves and just after the waves, the number of reported incidents decreased. Third, the number of incidents involving drug or blood transfusion and the number of monthly incidents of level 1 or 2 gradually decreased during the COVID-19 pandemic. Conclusions: The COVID-19 pandemic affected the contents and levels of reported incidents. Overall, the number of incident reports increased slightly during the pandemic compared to that before the pandemic, although not significantly, probably because medical staff were well informed and focused. Clin Infect Immun. 2024;9(1):1-10 doi: https://doi.org/10.14740/cii172
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".